Create vector index

Vector indexes allow Neo4j to run similarity queries across the whole database. The example below shows how to create a vector index for movie embeddings, under the name of moviePlots.

CREATE VECTOR INDEX moviePlots
FOR (m:Movie)
ON m.embedding
OPTIONS {indexConfig: {
 `vector.dimensions`: 384, (1)
 `vector.similarity_function`: 'cosine' (2)
}}

1 For embeddings generated with the SentenceTransformers model all-MiniLM-L6-v2. A different model may require a different dimension.

2 The cosine similarity function is the most common choice. For more details and other options, see Vector indexes → Cosine and Euclidean similarity functions.

CREATE VECTOR INDEX moviePlots
FOR (m:Movie)
ON m.embedding
OPTIONS {indexConfig: {
 `vector.dimensions`: 1536, (1)
 `vector.similarity_function`: 'cosine' (2)
}}

1 For embeddings generated with the OpenAI model text-embedding-ada-002. A different model may require a different dimension.

2 The cosine similarity function is the most common choice. For more details and other options, see Vector indexes → Cosine and Euclidean similarity functions.

For more information on vector indexes, see Cypher^® → Indexes → Vector indexes.